{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd5aadc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Video traffic</th>\n",
       "      <th>social traffic</th>\n",
       "      <th>Web browsing traffic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10942</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13382</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>1120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4192</td>\n",
       "      <td>1</td>\n",
       "      <td>2708</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10908</td>\n",
       "      <td>4</td>\n",
       "      <td>260</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14130</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>19995</td>\n",
       "      <td>18102</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>19996</td>\n",
       "      <td>18216</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>19997</td>\n",
       "      <td>18347</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>19998</td>\n",
       "      <td>18457</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>19999</td>\n",
       "      <td>18664</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>20000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0     ID  Video traffic  social traffic  Web browsing traffic\n",
       "0               0  10942             22              42                  1528\n",
       "1               1  13382             15              24                  1120\n",
       "2               2   4192              1            2708                     0\n",
       "3               3  10908              4             260                    15\n",
       "4               4  14130             35              28                     0\n",
       "...           ...    ...            ...             ...                   ...\n",
       "19995       19995  18102              6               0                     2\n",
       "19996       19996  18216              1               0                     0\n",
       "19997       19997  18347             14               0                     0\n",
       "19998       19998  18457            104               0                     0\n",
       "19999       19999  18664             17               0                     0\n",
       "\n",
       "[20000 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the necessary packages\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "sns.set()\n",
    "pd.options.display.max_columns = None\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "origin_data=pd.read_csv('./data/data_cluster.csv')\n",
    "origin_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c070edec",
   "metadata": {},
   "source": [
    "1.使用 sample()方法从 origin_data 中随机抽取了10000个样本，并允许重复抽样。重置索引，使得输出 data 为以下格式:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e5776dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 5)\n"
     ]
    }
   ],
   "source": [
    "#由考生填写\n",
    "data = origin_data.sample(10000)\n",
    "#由考生填写\n",
    "data.reset_index()\n",
    "print(data.shape)\n",
    "df_cluster = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc27a31",
   "metadata": {},
   "source": [
    "2.从 df_cluster 中选择所有 np.number 类型的列，并将其保存在df_X中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c59be744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'ID', 'Video traffic', 'social traffic',\n",
       "       'Web browsing traffic'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#由考生填写\n",
    "df_X = df_cluster.select_dtypes(include=np.number)\n",
    "#由考生填写\n",
    "cols = df_X.columns\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26ca5966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Video traffic</th>\n",
       "      <th>social traffic</th>\n",
       "      <th>Web browsing traffic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.943439</td>\n",
       "      <td>0.336801</td>\n",
       "      <td>0.008850</td>\n",
       "      <td>0.006429</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.063813</td>\n",
       "      <td>0.184428</td>\n",
       "      <td>0.230088</td>\n",
       "      <td>0.089740</td>\n",
       "      <td>0.259772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.798260</td>\n",
       "      <td>0.820623</td>\n",
       "      <td>0.176991</td>\n",
       "      <td>0.000536</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.782957</td>\n",
       "      <td>0.783868</td>\n",
       "      <td>0.061947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.854521</td>\n",
       "      <td>0.864630</td>\n",
       "      <td>0.141593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        ID  Video traffic  social traffic  Web browsing traffic\n",
       "0    0.943439  0.336801       0.008850        0.006429              0.000000\n",
       "1    0.063813  0.184428       0.230088        0.089740              0.259772\n",
       "2    0.798260  0.820623       0.176991        0.000536              0.000000\n",
       "3    0.782957  0.783868       0.061947        0.000000              0.000000\n",
       "4    0.854521  0.864630       0.141593        0.000000              0.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaled = pd.DataFrame(scaler.fit_transform(df_X))\n",
    "scaled.columns = cols\n",
    "scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6464388e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "k,silhouette,sses = [],[],[]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a05de27",
   "metadata": {},
   "source": [
    "3.使用 K-Means 模型，设置聚类数为i，初始化算法为k-means++，最大迭代次数为 500，初始化次数为 10，随机种子为 0，并将模型拟合到scaled 数据上。计算使用欧几里得距离度量标准的轮廓系数s1 和模型的质心间距离的总和 s2。输出如下所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e2fbff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K= 2 Silhouette = 0.34707577570973414 SSE = 1113.123776684965 distances= 0.5406595573543419\n",
      "K= 3 Silhouette = 0.36355884166221925 SSE = 752.5408004763037 distances= 0.4993544191438599\n",
      "K= 4 Silhouette = 0.3887228904264444 SSE = 528.604084031505 distances= 0.514412543248707\n",
      "K= 5 Silhouette = 0.3576960551641598 SSE = 454.2703317214559 distances= 0.9235699156337545\n",
      "K= 6 Silhouette = 0.3378511101471504 SSE = 391.25793122321176 distances= 0.6574512067184201\n",
      "K= 7 Silhouette = 0.32234607458684184 SSE = 355.2600212991051 distances= 0.7326224612525323\n",
      "K= 8 Silhouette = 0.32021364586595297 SSE = 327.19536052763397 distances= 0.6234445361306157\n",
      "K= 9 Silhouette = 0.31501730458629346 SSE = 305.42615166604355 distances= 1.0344937348269532\n",
      "K= 10 Silhouette = 0.3044065245605652 SSE = 288.02322707652525 distances= 0.6432645903533128\n",
      "K= 11 Silhouette = 0.29394152067185947 SSE = 270.74729644838817 distances= 0.5550285256107209\n",
      "K= 12 Silhouette = 0.29154934295497914 SSE = 255.53018345759176 distances= 0.9471500625225192\n",
      "K= 13 Silhouette = 0.2873505208665774 SSE = 242.2173224844343 distances= 0.7777266177404567\n",
      "K= 14 Silhouette = 0.2843983875439796 SSE = 232.23136387052855 distances= 0.7392976688220305\n",
      "K= 15 Silhouette = 0.2838903106549692 SSE = 223.57936792925224 distances= 0.3343414762467148\n",
      "K= 16 Silhouette = 0.28491396046289424 SSE = 216.19473970660277 distances= 0.5393410598873188\n",
      "K= 17 Silhouette = 0.26881822837631786 SSE = 209.75622217454315 distances= 0.6829421630583944\n",
      "K= 18 Silhouette = 0.2716028413424467 SSE = 202.44889033365223 distances= 0.7385839081083935\n",
      "K= 19 Silhouette = 0.2570433901006992 SSE = 197.74863155473085 distances= 0.7165355016579926\n"
     ]
    }
   ],
   "source": [
    "#由考生填写\n",
    "from sklearn.metrics import silhouette_score\n",
    "for i in range(2,20):\n",
    "    k_model = KMeans(n_clusters=i,init='k-means++',max_iter=500,n_init=10,random_state=0)\n",
    "    k_model.fit(scaled)\n",
    "    label = k_model.labels_\n",
    "    s1 = silhouette_score(X=scaled,labels=label)\n",
    "    sse = k_model.inertia_\n",
    "    cent = k_model.cluster_centers_\n",
    "    # np.linalg.norm是NumPy库中的一个函数，用于计算向量或矩阵的范数。\n",
    "    # 范数是一种衡量向量或矩阵大小的度量方式，在线性代数中有广泛的应用\n",
    "    s2 = np.sum(np.linalg.norm(cent[0]-cent[1],axis=0))\n",
    "    k.append(i)\n",
    "    silhouette.append(s1)\n",
    "    sses.append(sse)\n",
    "    print('K=',i,'Silhouette =',s1,'SSE =',sse,'distances=',s2)\n",
    "#由考生填写"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9d7285",
   "metadata": {},
   "source": [
    "轮廓系数，sse折线图： https://zhuanlan.zhihu.com/p/51777857"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e900fe8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画silhouette\n",
    "plt.figure()\n",
    "plt.title('K-means silhouette')\n",
    "plt.plot(range(2,20),silhouette,'*')\n",
    "plt.plot(range(2,20),silhouette ,'-',alpha=0.5) \n",
    "plt.xlabel('Number of Cluster')\n",
    "plt.ylabel('Silhouette')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8e66834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画sse\n",
    "plt.figure()\n",
    "plt.title('K-means SSE')\n",
    "plt.xlabel('Number of cluster')\n",
    "plt.ylabel('SSE')\n",
    "plt.plot(np.array(range(2,20)),sses,'o')\n",
    "plt.plot(np.array(range(2,20)),sses,'-',alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f65ed4b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
